Here \(S^g_i\)  and \(C^g_i\)  are downsampled versions of style and content guiding
channels (e.g., segmentation masks, see Figure REF ), \(D^g\)  is a
metric which evaluates guide similarity at pixels \(i\)  and \(j\)  (in our
experiments we use sum of squared differences), and \(w_{\cos }\)  and \(w_{guide}\) 
are weights that balance the influence of the cosine and guiding term (in our experiments we set \(w_{\cos }=0.5\)  and \(w_{guide}=0.5\) ). In
Figure REF  we demonstrate the effect of incorporating additional
segmentation masks as a guiding channels. Doing so forces features from the style to be transferred in a more predictable and semantically meaningful way. In
contrast to previous neural approaches that also support guidance [1]}, [2]} our technique better preserves visual aspects of the original
style exemplar.
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